The next iteration of the AMP-T2D Knowledge Portal

项目摘要 在本补充资料中，我们建议使用广泛使用的LD-Score回归(LDSC)软件包 人类遗传学界的工具，已被引用数千次--更符合目标 开放的科学。在我们最初的项目中，UM1DK105554(AMP-T2D知识的下一次迭代 门户“)，我们建议继续开发常见代谢疾病知识门户 (CMDKP)，加强其基础设施，以聚合、分析和可视化遗传和基因组数据集 和结果。作为这项工作的一部分，我们已将最不发达国家纳入CMDKP。LDSC计算的统计数据包括 GWAS数据集对，或GWAS数据集和基因组注释之间的数据，我们用它来帮助用户 查询和解释CMDKP中的数据。在将LDSC添加到CMDKP的过程中，我们做出了几个 修改其代码库以减少计算开销。这些反过来又确定了另外三个 可以对LDSC软件包进行改进，使其更符合以下目标 开放的科学。首先，通过在LDSC代码库中本地实现批处理数据集处理，我们将 使在云上轻松扩展LDSC成为可能，并使用户能够将其应用于他们自己的GWAS数据 跨越许多特征或注解。第二，通过实现用户可以访问的软件服务和API 上传他们自己的GWAS数据，然后获得跨性状关联或丰富所有 CMDKP中的数据集，我们将为LDSC提供一个新的传播渠道，目标是 不想下载任何数据或代码。第三，通过将LDSC重构为更模块化的库集合 现代语言，我们将使其更容易维护、扩展和重用。总的来说，这些改进 将通过使广泛使用的软件工具(A)可并行化、云就绪、重构 并且更具可扩展性；以及(B)通过易于使用的新软件服务和API提高可重用性。他们会 通过在CMDKP中提供与数据交互的新机制来推进我们原始项目的目标 并为其贡献新的数据集。

Project Summary In this supplement, we propose to make the LD-score regression (LDSC) software package – a widely used tool by the human genetics community that has been cited thousands of times – more compliant with the goals of open science. In our original project, UM1DK105554 (“The next iteration of the AMP-T2D Knowledge Portal”), we proposed to continue development of the Common Metabolic Disorders Knowledge Portal (CMDKP) by enhancing its infrastructure to aggregate, analyze, and visualize genetic and genomic datasets and results. As part of this work, we have added LDSC to the CMDKP. LDSC calculates statistics between pairs of GWAS datasets or between a GWAS dataset and a genomic annotation, which we use to help users query and interpret data in the CMDKP. In the process of adding LDSC to the CMDKP, we made several modifications to its codebase to reduce computational overhead. These in turn identified three additional improvements that could be made to the LDSC software package to make it more compliant with the goals of open science. First, by implementing batch dataset processing natively within the LDSC codebase, we would make it possible to easily scale LDSC on the cloud and enable users to apply it to their own GWAS data across many traits or annotations. Second, by implementing a software service and API to which users could upload their own GWAS data, and then obtain cross-trait correlations or annotation enrichments across all datasets in the CMDKP, we would provide a new dissemination channel for LDSC targeted at users who do not want to download any data or code. Third, by refactoring LDSC into more a modular set of libraries within a modern language, we would make it easier to maintain, extend, and reuse. Collectively, these improvements will advance open science by making a widely used software tool (a) parallelizable, cloud-ready, refactored, and more extensible; and (b) more reusable through an easy-to-use new software service and API. They will advance the goals of our original project by providing a new mechanism to interact with data in the CMDKP and contribute new datasets to it.